Six routes into the industry

Begin with how a machine represents a number and end able to reason about algorithmic complexity, memory, concurrency and databases. This is the path that makes every other path easier — and it is the material technical interviews at strong companies actually test.

Start at the command line with no prior experience and finish able to containerise an application, build a CI/CD pipeline, deploy it to Kubernetes, describe the infrastructure as code and know within seconds when it breaks. This is the path most people mean when they say they want to 'get into DevOps'.

Programming is making a computer do something. Software engineering is doing it in a team, on a codebase that outlives you, without breaking production. This path covers readable code, testing, design, APIs, security and the judgement that separates a developer who ships features from one who is trusted with a system.

SRE is what happens when you treat operations as a software problem. This path covers SLIs, SLOs and error budgets, observability that answers questions instead of producing dashboards, incident command, and the resilience patterns — timeouts, circuit breakers, backpressure, graceful degradation — that decide whether a bad afternoon becomes an outage.

The path into IT support, system administration and infrastructure. Start with how a computer and a network actually work, learn a troubleshooting method that holds up under pressure, then administer Windows and Linux, run networks, identity and cloud services, and secure the endpoints people work on every day. This is also the most common route into DevOps, SRE and security.

From your first dataframe to models running in production. Statistics that stop you fooling yourself, honest evaluation, feature engineering, and the parts nobody warns you about — leakage, imbalance, drift and explaining an uncertain result to someone who wants a yes or no. Built around the questions data science interviews actually ask.